Categorization of Meteorological Data by Contrastive Clustering (Papers Track)

Michael Dammann (HAW Hamburg); Ina Mattis (DWD); Michael Neitzke (HAW Hamburg); Ralf Möller (University of Lübeck)

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Unsupervised & Semi-Supervised Learning Climate Science & Modeling Computer Vision & Remote Sensing


Visualized ceilometer backscattering data, displaying meteorological phenomena like clouds, precipitation, and aerosols, is mostly analyzed manually by meteorology experts. In this work, we present an approach for the categorization of backscattering data using a contrastive clustering approach, incorporating image and spatiotemporal information into the model. We show that our approach leads to meteorologically meaningful clusters, opening the door to the automatic categorization of ceilometer data, and how our work could potentially create insights in the field of climate science.

Recorded Talk (direct link)